Self-learning Mutual Selection Model for Weighted Networks

Details Self-learning Mutual Selection Model for Weighted Networks Self-learning Mutual Selection Model for Weighted Networks

2005-12-30

arxiv.org/abs/physics/0512270v1

Physics

Physics and Society

5 pages, 5 figures

Scientific paper

In this paper, we propose a self-learning mutual selection model to characterize weighted evolving networks. By introducing the self-learning probability $p$ and the general mutual selection mechanism, which is controlled by the parameter $m$, the model can reproduce scale-free distributions of degree, weight and strength, as found in many real systems. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the nontrivial clustering coefficient $C$ and tunable degree assortativity $r$, depending on the parameters $m$ and $p$. The model can unify the characterization of both assortative and disassortative weighted networks. Also, we find that self-learning may contribute to the assortative mixing of social networks.

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